Full suppression of viral load is needed to achieve an optimal CD4 cell count response among patients on triple drug antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: To characterize the relationship between plasma viral load (pVL) suppression and triple drug antiretroviral therapy, and the accompanying changes in CD4 cell counts. METHOD: Retrospective study of 465 participants in a HIV/AIDS Treatment Program who initiated triple drug therapy between August 1996 and May 1998. Participants were divided into three groups according to their pVL response: (i) non-responders (NR; n = 112) exhibited pVL persistently > 500 copies/ml over the study period; (ii) partial responders (PR; n = 100) achieved a pVL < 100 copies/ml at least once and subsequently rebounded to > 500 copies/ml; and (iii) full responders (FR; n = 253) achieved a pVL < 500 copies/ml and sustained this level for the remainder of the study period. For each group, the accompanying changes in absolute and fractional CD4 cell counts were evaluated. RESULTS: The median net change in pVL per person from baseline to the end of the observation period was -0.37, -2.27, and -2.56 log10 copies/ml for NR, PR and FR, respectively. During weeks 68-83, the median CD4 cell count (x 10(6) cells/l) was 150 [interquartile range (IQR) 90-370], 380 (IQR 300-480) and 525 (IQR 305-705) for NR, PR and FR, respectively. Median changes in CD4 cells (x 10(6) cells/l) were -20 (IQR -90 to 40), 150 (IQR 30-250) and 240 (IQR 110-365) for NR, PR, and FR, respectively. The net percentage change in CD4 cells per person was 0% (IQR -34-31), 54% (IQR 6-160), and 83% (IQR 39-173) for NR, PR, and FR, respectively. By weeks 68-83, the median fractional CD4 cells was 0.16 (IQR 0.07-0.22), 0.22 (IQR 0.15-0.28), and 0.26 (IQR 0.17-0.34) for NR, PR and FR respectively. CONCLUSIONS: An optimal CD4 cell count response appears to be coupled with continued pVL suppression. Our data indicate that maximal suppression of viral replication should remain the primary goal of therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".